Preliminary insights from the EDU-LAB project

While many researchers, policy makers and educators throughout Europe share a common goal to help youth on their transition from education and training into the world of labour, their understanding of how to actually achieve that goal is not always a given. One of the main hurdles is surprisingly simple, yet fundamental – the data that we rely on don’t always reflect the entire picture and complexity of the aforementioned paths.

During the EDU-LAB (European Youth in Transition to Education and Labour) project’s first year we examined the data landscape that surrounds these transitions. A key part of this work involved analysing the datasets that many researchers and policymakers use.

These findings reveal an important starting point for future  research and policy discussions. While there is no question that Europe has many high quality datasets on education and labour markets, important gaps remain when it comes to understanding the full scope of transitions young people experience.

The report therefore identifies a differentiated pattern of “dataset gaps” rather than a single data problem

Looking at the Data Behind Youth Pathways

The EDU-LAB project’s goal is to understand how young people aged 15-30 navigate the worlds of education, training and the labour market.
In pursuit of this goal our team of researchers first had to analyse the existing datasets that are at the foundation of much of the research on the topic.

During our first year 20 major datasets related to education and labour-market pathways were examined. These include widely used large European surveys and international education datasets. The analysis was largely focused on a few key characteristics, including the data availability, the reliability of the methodology, and the degree to which the datasets reflect the factors and the transitions that influence the transitions from education and training into labour of young people.

At the end of the day, the analysis does confirm that many of the existing datasets are methodologically sound. However, it does reveal also gaps and substantial limitations that influence the actual ability of researchers to successfully analyse these transitions.

Missing the Long View: Limited Longitudinal Data

One of the most important findings is related to the availability of longitudinal data that follows individuals for a longer period of time through their transitions between education and training into labour and such data is crucial in understanding how young people manage these transitions. Without such longitudinal perspectives, it becomes difficult to understand how previous educational decisions influence later results.

What the analysis shows is that research on the longitudinal progression are relatively rare among the observed datasets, and that severely limits the possibility to analyse how young people's paths develop through time.

The analysis found that the datasets have restricted usability profiles across different forms of secondary analysis, and that longitudinal progression studies are described as “a lacking methodological option” among the datasets investigated.

Many existing research depends on cross-sectional data sets because of this. They encapsulate just one moment in time instead of following the full trajectory of educational and labour market transitions of youth.

Transitions That Are Hard to Observe

Another big challenge is related to the scope of specific types of transitions in educational systems, and the EDU-LAB analysis examines how the datasets capture the transitions between stages of education and the labour market.

Preliminary results are showing that some types of transitions are much better represented in the existing datasets, while others are considerably less visible.

The report identifies 24 Legally Allowed Transitions in Education and Training (LATET) and 8 additional generic transitions to the world of labour, to other institutions or to other countries. It also shows that coverage is uneven. In particular, the LATET transitions are described as “not well covered” by the datasets investigated, with the exception of one dataset that covers 12 such transitions. By contrast, the additional 8 transitions are reported to be well covered.

Datasets often show limited information about “legally allowed transitions” within education and training systems, which means the institutional paths which students can follow formally within the frame of national educational systems.

These transitions are very key in understanding how opportunities and choices are influenced by educational systems. When datasets don't reflect these well enough, it becomes more difficult to analyse how institutional rules form young people's paths.

Access to Data Is Not Always Straightforward

A further issue concerns the accessibility of datasets themselves and although many large datasets exist, they are not always easily available for detailed research.
We've come to realize that a significant part of the analysed datasets don't provide direct access to downloadable data and microdata for research purposes.

Here the report provides particularly clear figures: 65% of the 20 datasets investigated are reported as not downloadable, and 65% do not offer access to microdata for research.

This could limit the types of analysis that researchers are carry out and slow the development for new knowledge on the transitions between education and training and the labour market. Improving the accessibility of data could actually play a big role in amplifying such research.

Understanding the Factors That Shape Pathways

Beyond the characteristics of the datasets, the project has identified a wide array of determinants that influence educational and labour market pathways. These factors include social, institutional and individual elements that influence how young people receive access to education, take part in education, progress through different programmes, and ultimately complete their studies.

Our analysis has identified a wide range of such determinants across different stages of education and training pathways that highlight the complexity of the interactions that shape young people's opportunities and outcomes. Recognizing this complexity is crucial, as young people's transitions seldomly follow a single linear trajectory, and the factors that define them often influence each other in a more dynamic manner.

The Year One work identified 80 intersectional determinants, organised into 4 groups: General Selection, Access, Participation, and Progression and Completion. These groups are used to structure the analysis of how datasets cover the factors that may shape young people’s pathways in education and training and in transitions to the labour market.

Building the Foundations for Future Research

The analysis of the data sets that was conducted in the first year of EDU-LAB does not try to give definitive answers on the transitions of young people from education and training into the labour market, but instead, it creates a crucial foundation for the next stages of the project.

By identifying the areas where existing data sets offer good coverage and those where many gaps are identified, this research helps guide us in future analysis within the scope of the project.
It also underlines broader challenges that researchers and policymakers face when trying to understand how educational systems connect to the labour market.

In the coming phases, our project will build upon this foundation by combining data set analysis with policy reviews, expert interviews, and qualitative case studies involving young people themselves. These different approaches to research together aim to provide a more comprehensive understanding for how young people transition from education and training to employment across Europe.

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